Like many industries, the contact center represents the primary interaction channel in the healthcare industry for various “customers,” whether they are patients or providers. But perhaps unlike any other industry, service providers in this industry are entrusted with the heavy responsibility of providing quick, responsive, and accurate service to people who count on them for potentially life-and-death matters. And unlike other industries where transactions are relatively straightforward—such as booking a reservation—people in this industry provide a wide range of services, which can often be extremely complex and time-consuming, such as the process of filing a health insurance claim. When you add other issues into the mix, such as mounting costs, privacy concerns, and compliance risks, it’s easy to see why delivering cost-effective, high-quality service in this industry has been such a persistent challenge.

Taken together, it’s clear that speech analytics is particularly well-suited to addressing service challenges in the healthcare industry. A number of common threads are starting to emerge from the early-adopter successes that will likely accelerate industry adoption and contact center operational sophistication as they evolve to address these pressing challenges.

For instance, rising call volume and spiraling costs are huge challenges. A number of BlueCross organizations—from Excellus BlueCross BlueShield of New York to BlueCross of Northeastern Pennsylvania (BCNEPA) to BlueCross BlueShield of Tennessee—have turned to speech analytics to answer a host of unanswered questions. Instead of having to rely on anecdotal evidence, speech analytics gives them visibility from a big picture, bird’s-eye operational view as well as a granular, root-cause-analysis level. With the ability to segment out call types, they can finally answer fundamental questions, such as:

• What’s really driving up our call volume and why?
  
• Where should we focus our efforts? Which types of calls are accounting for a disproportionate amount of handling time and why?
  
• Where are the key improvement opportunities for reducing communication and service breakdowns, addressing agent behavioral or process issues, deflecting calls to self-service channels, and other key concerns?
  

More specifically, for these organizations, first-call resolution is a key metric into which they had little visibility and understanding. One organization was shocked when a third-party consultancy determined that its first-call resolution rate was only in the 60 percentile. Since it used so-called universal agents who were cross-trained to handle all types of calls, it had estimated much higher success rates and was puzzled as to why this metric was so low. After it brought us in and we segmented out the calls by call type, we were able to validate the low rate and determine that calls relating to claims processing were one of the main culprits. Unlike enrollment-related calls, of which about one in five contacts resulted in a callback, one out of every four claims-related calls resulted in at least one callback. Worse, each of these calls averaged more than 10 minutes in length and accounted for approximately 30 percent of total talk time.

After further investigation, the organization was able to understand what percentage of calls resulted in follow-up calls due to the fact that claims were reprocessed, rejected, or resubmitted. With this level of detail, it can take this insight to drive real change. The organization was able to build a report that benchmarked first-call resolution by various dimensions; it could determine which providers are accounting for a disproportionate volume of calls and then work more closely with them to determine why. Or it could segment reports by agents to determine, for example, if a new agent wasn’t following proper procedure or was generating excessive errors.

To give another example, speech analytics enabled one organization to determine that more than 5 percent of its calls resulted in a “negative customer experience,” which even worse, translated into 11 percent of total talk time (or more than $3 million a year in service costs). Again, it was able to drill down and pinpoint the various root causes, which included agents who failed to call back as promised as well as a number of misunderstandings around exact plan coverages.

To give a member satisfaction example, one organization leveraged speech analytics to boost member satisfaction. Before speech analytics, it simply didn’t have the manpower to manually sift through the enormous call volume. But after implementation, it deployed a “proactive customer response process,” a kind of turbocharged quality assurance team. This group uses targeted queries to sift through calls every day to indentify potential issues. If it found, for instance, that an agent handled a call improperly, it would give that agent targeted feedback and/or training. What’s more, it also calls members back directly to apologize for the poor handling and works to resolve the issues to their satisfaction. And the best part is that it was able to directly impact satisfaction with only a few people.

With so many unseen opportunities for improvement, the possibilities for efficiency and cost-saving opportunities are readily abundant. And isn’t that what investing in speech technology is about—leveraging technology to get the greatest bang for your buck?

Mike Hutchison is vice president of professional services at Nexidia.